QTM 447 Lecture 28: Ethics

\[ \newcommand\hbb{{\hat{\boldsymbol \beta}}} \newcommand\bb{{\boldsymbol \beta}} \newcommand\expn{{\frac{1}{N} \sum \limits_{i = 1}^N}} \newcommand\sumk{\sum \limits_{k = 1}^K} \newcommand\argminb{\underset{\bb}{\text{argmin }}} \newcommand\argmaxb{\underset{\bb}{\text{argmax }}} \newcommand\gtheta{\mathbf g(\boldsymbol \theta)} \newcommand\htheta{\mathbf H(\boldsymbol \theta)} \]

Projects

If you want your poster shown off at the event tomorrow, get me your poster ASAP.

• If not, then turn it in on Canvas by the deadline tomorrow

I really think that having an opportunity to show off will really put you in a place where you can realize just how much stuff you know

• And how impressive that is to the general public.

Projects

Final deliverable: Paper or website for your project

• Due May 7th

• Need to see that you actually did something

• Should serve as a base for a paper or project that you can continue later

• Can’t be late. Last day to grade and enter grades for graduating seniors

• What I care about the most.

Projects

I’ll hold regular office hours up until the 7th.

• Email me if you want to chat at a different time.

Modern AI

Modern AI

Modern AI

Modern AI

Modern AI

Modern AI methods are accessible

• Even my mother knows about ChatGPT!

Most have no idea how they work and why they do what they do.

• Not you, though. You know now.

Modern AI

Modern AI

Ethics for Modern AI

A new ethical code is needed for highly accessible generative AI

Ethics for Modern AI

Ethics for Modern AI

Ethics for Modern AI

Ethics for Modern AI

It’s not only harm that can be caused at a macro level.

Individual’s can easily be harmed:

• Personalized cyberbullying

• Deepfakes

• Catfishing

• Sextortion

Ethics for Modern AI

Is there a general set of rules for how we should leverage/gate modern generative AI?

• A big question.

• Do philosophical frameworks of ethics help us here?

Utilitarianism

An ethical framework centered on outcomes

Make choices that do the greatest good for the greatest number of people

• A minimax problem:

  • Maximize happiness

  • Minimize harm

Utilitarianism

Strengths:

• Practical and outcome focused

• Encourages consideration of broad societal impacts

• Clear criterion (maximize welfare)

Weaknesses:

• Requires quantification

• Rewards the majority, penalizes the minority

• Can justify unethical means to a “positive” end

Deontology

An ethical framework focused on duties, rights, and universal ethical rules

• Irrespective of outcomes

Contraints placed on the means, not the ends.

Ethical actions respect universal moral duties

• Honesty

• Fairness

• Consent

Deontology

Open-sourcing powerful generative AI models like Stable Diffusion

• What would deontology say here?

Deontology

Strengths:

• Clarity in ethical duties

• Protects individual rights

• Promote consistency

Weaknesses:

• Sometimes the end justifies the means!

• No conclusions possible when duties conflict

Ethics?

Virtue Ethics

An ethical framework focusing on moral character and virtues

• Not rules and consequences, alone

What would a morally virtuous person do?

Virtue Ethics

In our craw, we know the difference between right and wrong

Decisions must be made based upon our ability to reason through the moral implications.

Do our best to meet a set of moral goods.

Pillars

Fairness

• AI should produce things that promote equity

• The world we want, not the world we live in

Treat individuals and groups without discrimination

• AI amplifies existing societal biases if unchecked

• Unfair outcomes erode trust in AI systems

Pillars

Pillars

Pillars

How can we address problems of fairness in AI?

Pillars

Datasheets for Datasets

• Do you think that this is enough?

• How can we know if our training set will lead to bias?

• How can we correct it?

Pillars

Truthfulness

AI systems should produce content and information that accurately represents reality and transparently discloses its artificial origin

Pillars

Tom Cruise?

This one is harder:

• Turthfulness is a combination of intent and algorithmic design

• More complexity = more truthfulness

• More complexity = more ability to create untruths in a convincing way

Proposals?

Pillars

Consent

AI should respect individual autonomy by obtaining explicit permission before collecting, using, or generating representations of their data or identity.

Struggles

• How do we collect consent?

• Is Instagram fair game? You did consent.

• Do IP laws apply to AI?

Pillars

Github Copilot:

• Trained on all code in Github repos

• All code is there

• Even proprietary code

• But, Github has some sense of ownership

Thoughts?

Pillars

AI Art:

Pillars

How are we able to replicate a copyrighted style?

• How can we prevent this?

• Does it stifle the capability of AI art?

• Is that a bad thing?

Virtue Ethics

Open-sourcing powerful generative AI models like Stable Diffusion

• What would your moral core say here?

The truth

We don’t have good answers for this!

Wrap-Up

We’ve covered a lot this semester!

Machine learning theory:

• Complexity theory (VC dimensionality)

• Bias variance tradeoffs

• Bayesian statistics

• Regularization

• Universal Approximation Theory

• Convex optimization and stochastic minimization

Wrap-Up

Basic Neural Networks:

• Multilayer Perceptrons

• Deep Neural Networks

• Activations for Nonlinearities

• Generalization Methods

Wrap-Up

NNs for Images:

• CNNs

• Residual Networks and Batch Normalization

• Modern CNN architectures

• Bounding box detection

• Semantic segmentation and UNets

Wrap-Up

NNs for Sequences:

• RNNs

• LSTMs

• Seq2Seq

• Attention

• Self-Attention and Transformers

• BERT and GPT

Wrap-Up

Deep Generative Methods:

• Autoregressive Models

• Variational Autoencoders

• Generative Adversarial Networks

• Diffusion Models

Wrap-Up

The pace of this class was incredibly ambitious and you all kept up with the materials admirably!

Wrap-Up

As a second go at this class, there was a mixture of good and bad (from my perspective)

Good:

• Lecture order flowed pretty well

• Y’all were quite engaged with the material

• We covered very modern topics

• A broad overview of what we can do with NNs

• Good projects!

Wrap-Up

As a second go at this class, there was a mixture of good and bad (from my perspective)

Bad:

• The homeworks got slowed down by resource issues/ability to put together meaningful examples that could run in finite time that weren’t too simple

• Homeworks should be more like guided projects than anything. But, that takes too much time. Gotta figure it out.

• I’ve learned that I don’t actually like PyTorch Lightning as much as I thought I would and am going to switch to base PyTorch for next year.

Wrap-Up

It’s been a learning process…

• But, I’m looking forward to taking the work and feedback from this semester and improving it in years to come!

On that note, please fill out the course eval

• It matters a lot for my career

• Your comments good and bad (though not personal ones about the fact that I only have two sweatshirts and two pairs of jeans) are really valuable for my development as a teacher and the overall development of this class

Wrap-Up

I have really enjoyed teaching this class

• I thought I knew a lot before the semester started, but I learned sooooo much prepping this material!

• This is my favorite class I’ve ever designed from the ground up

• I’m looking forward to doing this again!

Wrap-Up

To the graduating seniors:

• Congrats!

• This is my fourth year here, so I met some of y’all in my first year teaching

• You are all so impressive and I know you’re going to do amazing things with your lives!

To those not graduating:

• Please don’t be a stranger

• I’ll still be around next year, so please stop by and let me know how things are going

Wrap-Up

I have genuinely enjoyed getting to know each and every one of you

• You are all incredibly bright and pretty good programmers

• I am really optimistic about the next wave of data scientists :)